A coral‐reef optimization algorithm for the optimal service distribution problem in mobile radio access networks

Abstract

Mobile technology is currently one of the main pillars of worldwide economy. The constant evolution that mobile communications have undergone in the last decades, due to the appearance of new services and new technologies such as Universal Mobile Telecommunication Systems/High Speed Data Access and Long Term Evolution, has contributed to achieve this position in global economy. However, because of the crisis of the sector in the last 5 years, mobile operator's revenues and investments have been reduced. Thus, mobile network operators tend to exploit the existing infrastructure at maximum possible, trying to use the existing network in the most efficient way. In this paper, a novel bio-inspired algorithm, the coral reef optimization algorithm (CRO) is introduced to minimise a network deployment investment cost problem. This is carried out by means of optimising the user demand of different services offered by mobile operators over the available technologies in the market, namely the optimal service distribution problem. The CRO is a recently proposed meta-heuristic based on the computer simulation of corals reproduction and reefs' formation. In this paper, this algorithm has been tested on several optimal service distribution problem scenarios in Spain, observing a significant reduction (up to 400 Me) on the total investment costs associated to the radio access network deployment. We compare the performance of the CRO approach with that of a classical (experience-based) services distribution, and with alternative meta-heuristics techniques, obtaining good results in all cases.

A novel multi-objective Interactive Coral Reefs Optimization algorithm for the Unequal Area Facility Layout Problem

“…The CRO finally results in a kind of combination of Simulated Annealing and Evolutionary Algorithms [12]. This strategy has been able to outperform other meta-heuristics algorithms in many different areas such as, for example, Bio-medical applications [13,14], Telecommunications [15,16], Structural Engineering [17,18] or Energy [19,20]. Furthermore, the CRO has been successfully applied to other hard optimization problems such as resource allocation problems [21], neural network training [22], clustering [23] and time series analysis [24].…”

A coral-reefs and Game Theory-based approach for optimizing elastic cloud resource allocation

“…In particular, in each step of the CRO algorithm, a set of coral larvae is generated, and each larva must fight to obtain a place in the reefs. The result of this fight is that some corals die, because they cannot defend the place where they are located, as well as not all larvae find any place in the reefs where they can be settled [41]. It depends on how strong the larva is, i.e., how good the solution to the optimization problem is.…”

A Hybrid Coral Reefs Optimization—Variable Neighborhood Search Approach for the Unequal Area Facility Layout Problem

“…In the last years, the use of new meta-heuristic strategies has been raising. Specifically, the coral reef optimization (CRO) algorithm has been successfully used in different kind of problems as, for example, the optimal layout of turbines in wind farms [43], wind speed prediction [44], solar radiation prediction [45], prediction of the total energy demand of a nation [46], optimal distribution of different services in mobile communications systems [47], maximization of the network coverage [48]; minimization of the installation cost, and minimization of the electromagnetic pollution caused by the installation of new base stations [49], image thresholding [50], and wifi channel assignment [51], among others. In this context, the CRO has been recently applied to the UA-FLP successfully, improving most of the previously known results by means of combining the CRO with island evolution [52] and multiobjective interactive evolution [53].…”